Mean Square Convergent Particle Swarm Optimization Based on Fuzzy Migratory Operator

Guorong Cai (), Shaozi Li (), Shuili Chen () and Songzhi Su ()
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Guorong Cai: School of Science, Jimei University, Xiamen 361021, P. R. China;
Shaozi Li: College of Information Science and Technology, Xiamen University, Xiamen 361005, P. R. China;
Shuili Chen: School of Science, Jimei University, Xiamen 361021, P. R. China;
Songzhi Su: College of Information Science and Technology, Xiamen University, Xiamen 361005, P. R. China;

New Mathematics and Natural Computation (NMNC), 2014, vol. 10, issue 02, 163-175

Abstract: This paper proposes a novel cooperative particle swarm PSO (particle swarm optimization)algorithm, which makes the use of the property of the fuzzy migratory operator to achieve the optimization performance. To avoid the drawback of the possibility of being trapped in local optimum, the proposed method uses a migrate-based strategy to control the diversity of the swarm. During the iteration aspect of the algorithm, the comprehensive fuzzy evaluation method is employed to evaluate the diversity. Furthermore, the fuzzy migratory operator is then used to remove bad particles once the diversity is far from ideal. Moreover, we have proven that the proposed migrate strategy is a mean square convergence. The experimental results conducted on three benchmark functions also proved that the proposed method is superior to that of classical PSO and conventional cooperative PSO, where the comparison has been based primarily upon the global optimality, solution accuracy and diversity value.

Keywords: Particle warm optimization; fuzzy migratory operator; population entropy; comprehensive fuzzy evaluation (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S1793005714500082

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